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A method for deriving displacement data during cyclical movement using an inertial sensor.
Abstract: Biomechanical studies often employ optical motion capture systems for the determination of the position of an object in a room-based coordinate system. This is not ideal for many types of study in locomotion since only a few strides may be collected per ;trial', and outdoor experiments are difficult with some systems. Here, we report and evaluate a novel approach that enables the user to determine linear displacements of a proprietary orientation sensor during cyclical movement. This makes experiments outside the constraints of the laboratory possible, for example to measure mechanical energy fluctuations of the centre of mass during over-ground locomotion. Commercial orientation sensors based on inertial sensing are small and lightweight and provide a theoretical framework for determining position from acceleration. In practice, the integration process is difficult to implement because of integration errors, integration constants and the necessity to determine the orientation of the measured accelerations. Here, by working within the constraints of cyclical movements, we report and evaluate a method for determining orientation and relative position using a modified version of a commercial inertial orientation sensor that combines accelerometers, gyroscopes and magnetometers, thus giving a full set of movement parameters (displacement, velocity and acceleration in three dimensions). The 35 g sensor was attached over the spine of a horse exercising on a treadmill. During canter locomotion (9.0 m s-1), the amplitudes of trunk movement in the x (craniocaudal), y (mediolateral) and z (dorsoventral) directions were 99.6, 57.9 and 140.2 mm, respectively. Comparing sensor displacement values with optical motion capture values for individual strides, the sensor had a median error (25th, 75th percentile) in the x, y and z directions of 0.1 (-9.7, +10.8), -3.8 (-15.5, +13.7) and -0.1 (-6.3, +7.1) mm, respectively. High-pass filtering of the displacement data effectively separated non-cyclical from cyclical components of the movement and reduced the interquartile ranges of the errors considerably to (-3.6, 6.2), (-4.0, 3.8) and (-4.5, 5.1) for x, y and z displacement, respectively, during canter locomotion. This corresponds to (-3.2, 5.5)%, (-6.7, 6.3)% and (-3.3, 3.7)% of the range of motion.
Publication Date: 2005-06-18 PubMed ID: 15961737DOI: 10.1242/jeb.01658Google Scholar: Lookup The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
- Comparative Study
- Journal Article
- Research Support
- Non-U.S. Gov't
Summary
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The research presents a new method for deriving displacement data during cyclical movements, such as walking or running, using an inertial sensor. This approach allows for more accurate measurements outside of a laboratory setting, overcoming the limitations of traditional motion capture systems.
Research Context
- The study is based in the field of biomechanics where movement and activity are often studied using optical motion capture systems.
- These systems can track the position of an object within a coordinate system of a particular room or space. However, the practicality of these systems is limited, especially for locomotion studies, as they cannot record for extended periods, confining them to only a few strides per trial. They also pose challenges for outdoor experiments.
New Approach
- The researchers propose an innovative approach that uses a proprietary orientation sensor to determine linear displacements during cyclical movements.
- The scientists aim to measure mechanical energy fluctuations of the center of mass during over-ground locomotion, which was previously challenging with existing data capture systems.
- This new method allows for experiments to be conducted outside of the laboratory, broadening their scope and applicability.
Sensor and Theoretical Framework
- The sensor used in the study is commercially available, based on inertial sensing, and comes with a theoretical framework for determining position from acceleration.
- However, the integration process of determining orientation from the measured accelerations posed initial obstacles due to integration errors and constants.
- To solve this, the team developed a method for determining orientation and relative position using a modified version of the inertial orientation sensor, combining accelerometers, gyroscopes, and magnetometers. This gives a comprehensive set of movement parameters, capturing displacement, velocity, and acceleration in three dimensions.
Implementation and Results
- The researchers attached the sensor to the spine of a horse exercising on a treadmill to gather data during canter locomotion.
- Comparisons were drawn between displacement values derived from the sensor and the values extracted from optical motion capture systems. The median error in displacement values was found negligible.
- High-pass filtering was employed to the displacement data, effectively separating non-cyclical from cyclical components of the movement. This significantly reduced the error range during canter locomotion.
Conclusion
- This novel approach of deriving displacement data using an inertial sensor displays promising results for expanding the scope and effectiveness of locomotion and other biomechanical studies.
Cite This Article
APA
Pfau T, Witte TH, Wilson AM.
(2005).
A method for deriving displacement data during cyclical movement using an inertial sensor.
J Exp Biol, 208(Pt 13), 2503-2514.
https://doi.org/10.1242/jeb.01658 Publication
Researcher Affiliations
- Structure and Motion Laboratory, The Royal Veterinary College, Hawkshead Lane, Hatfield, Hertfordshire, AL9 7TA, UK. tpfau@rvc.ac.uk
MeSH Terms
- Acceleration
- Animals
- Biomechanical Phenomena / instrumentation
- Biomechanical Phenomena / methods
- Evaluation Studies as Topic
- Horses / physiology
- Locomotion / physiology
- Monitoring, Ambulatory / instrumentation
- Monitoring, Ambulatory / methods
- Signal Processing, Computer-Assisted
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